Abstract: Fiber optic surface plasmon resonance (FO-SPR) has shown its potential for the detection of nucleic acids and more recently the technology has been combined with catalytic active strands such as DNAzymes. In this work, an innovative, generic solid-phase DNA sensor concept is presented, based on FO-SPR and PCR amplified DNAzyme activity. Improved levels of specificity and sensitivity were obtained down to picomolar concentrations. Moreover, the FO-SPR sensor concept enables AuNP amplified DNA target detection, independent of the target sequence length. The FO-SPR sensor was demonstrated for the screening of the mobile colistin resistance (MCR-2) gene, a gene important for the antimicrobial resistance in Gram-negative species such as E. Coli.

Abstract: We model transport through two-dimensional topological insulator (TI) nanoribbons. To model the quantum transport, we employ the non-equilibrium Green's function approach. With the presented approach, we study the effect of lattice imperfections on the carrier transport. We observe that the topologically protected edge states of TIs are robust against a high percentage (2%) of vacancy defects. We also investigate tunneling of the edge states in two decoupled TI nanoribbons.

Abstract: As the field of perovskite optoelectronics developed, a plethora of strategies has arisen to control their electronic and morphological characteristics for the purpose of producing high efficiency devices. Unfortunately, despite this wealth of deposition approaches, the community experiences a great deal of irreproducibility between different laboratories, batches and preparation methods. Aiming to address this issue, we developed a simple deposition method based on gas quenching that yields smooth films for a wide range of perovskite compositions, in single, double, triple and quadruple cation varieties, and produces planar heterojunction devices with competitive efficiencies, so far up to 20%.

Abstract: We present a computational imaging mode for large scale electron microscopy data, which retrieves a complex wave from noisy/sparse intensity recordings using a deep learning approach and subsequently reconstructs an image of the specimen from the Convolutional Neural Network (CNN) predicted exit waves. We demonstrate that an appropriate forward model in combination with open data frameworks can be used to generate large synthetic datasets for training. In combination with augmenting the data with Poisson noise corresponding to varying dose-values, we effectively eliminate overfitting issues. The U-NET[1] based architecture of the CNN is adapted to the task at hand and performs well while maintaining a relatively small size and fast performance. The validity of the approach is confirmed by comparing the reconstruction to well-established methods using simulated, as well as real electron microscopy data. The proposed method is shown to be effective particularly in the low dose range, evident by strong suppression of noise, good spatial resolution, and sensitivity to different atom types, enabling the simultaneous visualisation of light and heavy elements and making different atomic species distinguishable. Since the method acts on a very local scale and is comparatively fast it bears the potential to be used for near-real-time reconstruction during data acquisition.

Abstract: Intending to tackle road traffic noise in urban environments, noise barriers have been proven to effectively reduce environmental noise levels, leading to positive effects on noise perception by the exposed population. This work assesses the impacts of replacing an obsolete noise barrier in a site near a highway. The effects of this change were monitored via a combination of field surveys, acoustic measurements and noise maps. The results have shown that even though the barrier replacement led to a 4.1 dB reduction in the LA,eq,(15 min.), the annoyance levels of the respondents increased. Possibly, the expectations regarding the improvement of the noise barrier were not met, after a history of complaints. Additionally, existing exposure-response relationships were not successful in predicting the annoyance levels in this particular case. In this dataset, noise annoyance presented a weak link with reported health problems, while a strong correlation was found with the comfort level to perform activities outdoors. Questions regarding the COVID-19 pandemic showed that even though the respondents were spending more time at home, they were less annoyed due to road traffic noise in the period when circulation restrictions were in place.

Abstract: The fine balance between dipole-field energy and anion drift force defines the switching mechanism during polarization reversal: for the first time we show that only Pbcm mechanism obeys the ferroelectric switching physics, whereas P4(2)/nmc (or any other) mechanism does not. However, with lower energy barrier, it represents an important antiferroelectric mechanism. Constraints relaxation can lead to 90 degrees polarization rotation (domain deactivation). Intrinsically, the Si/VO-doping can switch faster than undoped HfO2 or HfZrOx. Theoretical Arrhenius model / intrinsic material switching (DFT) overestimates the switching speed extracted from experiments.

Abstract: Electroporation is a non-thermal electromagnetic phenomenon widely used in medical diseases treatment. Different mathematical models of electroporation have been proposed in literature to study pore evolution in biological membranes. This paper presents a nonlinear dispersive multiphysic model of electroporation in irregular shaped biological cells in which the spatial and temporal evolution of the pores size is taken into account. The model solves Maxwell and asymptotic Smoluchowski equations and it describes the dielectric dispersion of cell media using a Debye-based relationship. Furthermore, the irregular cell shape has been modeled using the Gielis superformula. Taking into account the cell in mitosis phase, the electroporation process has been studied comparing the numerical results pertaining the model with variable pore radius with those in which the pore radius is supposed constant. The numerical analysis has been performed exposing the biological cell to a rectangular electric pulse having duration of 10 μs. The obtained numerical results highlight considerable differences between the two different models underling the need to include into the numerical algorithm the differential equation modeling the spatial and time evolution of the pores size.

Abstract: In this work, a nonlinear dispersive multiphysic model based on Maxwell and asymptotic Smoluchowsky equations has been developed to analyze the electroporation phenomenon induced by pulsed electric field on biological cells. The irregular plasma membrane geometry has been modeled by incorporating in the numerical algorithm the Gielis superformula as well as the dielectric dispersion of the plasma membrane has been modeled using the multi-relaxation Debye-based relationship. The study has been carried out with the aim to compare our model implementing a thin plasma membrane with the simplified model in which the plasma membrane is modeled as a distributed impedance boundary condition. The numerical analysis has been performed exposing the cell to external electric pulses having rectangular shapes. By an inspection of the obtained results, significant differences can be highlighted between the two models confirming the need to incorporate the effective thin membrane into the numerical algorithm to well predict the cell response to the pulsed electric fields in terms of transmembrane voltages and pore densities, especially when the cell is exposed to external nanosecond pulses.

Abstract: Electromagnetic design problems involve optimizing multiple parameters that are nonlinearly related to objective functions. Traditional optimization techniques require significant computational resources that grow exponentially as the problem size increases. Therefore, a method that can produce good results with moderate memory and computational resources is desirable. Bioinspired optimization methods, such as particle swarm optimization (PSO), are known for their computational efficiency and are commonly used in various scientific and technological fields. In this article we explore the potential of advanced PSO-based algorithms to tackle challenging electromagnetic design and analysis problems faced in real-life applications. It provides a detailed comparison between conventional PSO and its quantum-inspired version regarding accuracy and computational costs. Additionally, theoretical insights on convergence issues and sensitivity analysis on parameters influencing the stochastic process are reported. The utilization of a novel quantum PSO-based algorithm in advanced scenarios, such as reconfigurable and shaped lens antenna synthesis, is illustrated. The hybrid modeling approach, based on the unified geometrical description enabled by the Gielis Transformation, is applied in combination with a suitable quantum PSO-based algorithm, along with a geometrical tube tracing and physical optics technique for solving the inverse problem aimed at identifying the geometrical parameters that yield optimal antenna performance.

Abstract: The study presents a 2D fluid plasma model of a gliding arc discharge for dissociation of CO2 which allows its subsequent conversion into value-added chemicals. The model is based on the balance equations of charged and neutral particles, the electron energy balance equation, the gas thermal balance equation and the current continuity equation. By choosing the modeling domain to be the plane perpendicular to the arc current, the numerical calculations are significantly simplified. Thus, the model allows us to explore the influence of the gas instabilities (turbulences) on the energy efficiency of CO2 conversion. This paper presents results for plasma parameters at different values of the effective turbulent thermal conductivity leading to enhanced energy transport.

Abstract: We discuss the selection and assessment of alternative metals by a combination of ab initio computation of electronic properties, experimental resistivity assessments, and calibrated line resistance models. Pt-group metals as well as Nb are identified as the most promising elements, with Ru showing the best combination of material properties and process maturity. An experimental assessment of the resistivity of Ru, Ir, and Co lines down to similar to 30 nm(2) is then used to devise compact models for line and via resistance that can be compared to Cu predictions. The main advantage of alternative metals originates from the possibility for barrierless metallization.